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Abstract
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We introduce a log-linear regression model based on the odd loglogistic
generalized half-normal distribution [7]. Some of its structural
properties including explicit expressions for the density function,
quantile and generating functions and ordinary moments are
derived. We estimate the model parameters by the maximum likelihood
method. For different parameter settings, proportion of censoring
and sample size, some simulations are performed to investigate
the behavior of the estimators. We derive the appropriate matrices
for assessing local influence diagnostics on the parameter estimates
under different perturbation schemes. We also define the martingale
and modified deviance residuals to detect outliers and evaluate the
model assumptions. In addition, we demonstrate that the extended
regression model can be very useful in the analysis of real data and
provide more realistic fits than other special regression models. The
potentiality of the new regression model is illustrated by means of a
real data set.
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